Improvement of snow physical parameters retrieval using SAR data in the Arctic (Svalbard)
Bibliographic record
Abstract
Arctic snow cover dynamics offer a changing face in terms of temporal duration and water equivalent, due to recent climate change conditions (Callaghan et al., 2011; Lemke & Jacobi, 2012). Indeed, the Arctic is now experiencing some of the most rapid and severe climate change on earth. In this context, innovative and improved methods are helpful to enhance management of the snow-pack resource for climate research, hydrology and human activities. The characteristics of Arctic snow are different from “temperate” snow (i.e. the Alps), in terms of thickness, internal structure, thermal conductivity, and metamorphism. Ground observation often indicates wind slab at the snow surface, internal rounded grains, depth hoar at the bottom, and often internal ice layer or at the Interface with ground surface (Dominé et al., 2016; Gallet et al., 2017, for spring snow). This work is part of the “Precip-A2” project (OSUG, Grenoble-France), focusing on snow and its interaction with the atmosphere, especially in terms of chemistry, radiative processes and precipitation. The application site is the Brøgger peninsula, focused on Ny-Ålesund area, Svalbard, Norway (N 78°55’ / E 11° 55’). One sub-task of the Precip-A2 project is dedicated to X-band radar measurements (ground and spaceborne) to retrieve physical properties of arctic snow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".